Title: ArrayTrack --- Data management, analysis and interpretation tool for DNA microarray and beyond
1ArrayTrack--- Data management, analysis and
interpretation tool for DNA microarray and
beyond
2ArrayTrack A brief history in the 5 years
Development Cycle
- AT version 1 (2001)
- Filter array data management tool
- AT version 2 (2002) in-house microarray core
facility - Customized two color arrays data management,
analysis and interpretation - Open to public (late of 2003)
- AT version 3.1 (2004) VGDS
- Affymetrix analysis capability enhanced
- AT version 3.2 (2005) MAQC
- Tested on 7 commercial platforms (Affy, Agilent
one- and two-color arrays, ABI, CodeLink,
Illumina ) - Integrated with other software (IPA, MetaCore,
DrugMatrix, CEBS, SAS/JMP ) - AT version 4 (2006 present)
- CDISC/SEND standard
- VGDS ? VXDS
3ArrayTrack Client-Server Architecture
CLIENT
Analysis Tools
Pub data (Gene annotation, Pathways )
Study data (Clinical and non-clinical data)
Microarray Proteomics Metabolomics
SERVER
CDISC/SEND
MIAME
NCBI, KEGG, GO
4ArrayTrack An Integrated Solution
Clinical and non-clinical data
Chemical data
ArrayTrack
5ArrayTrack Website
http//www.fda.gov/nctr/science/centers/toxicoinfo
rmatics/ArrayTrack/
6ArrayTrack MicroarrayDB-LIB-TOOL- An integrated
environment for microarray data management,
analysis and interpretation
TOOL
Microarray DB
LIB
7ArrayTrack for Microarray Data Management and
Analysis
Hypothesis
Exp Design
Microarray Exp
Data management
Data analysis
Data interpretation
8MicroarrayDB Storing data associated with a
microarray exp
- Microarray database
- Handling both one- and two-channel data,
including affy data - Only the CEL file is required for affy data
- Supporting toxicogenomics research by storing tox
parameters, e.g., dose schedule and treatment,
sacrifice time - MIAME supportive to capture the key data of a
microarray experiment - Will be MAGE-ML compliant to ensure inter-
exchangeability between ArrayTrack and other
public databases
Microarray DB
9LIB Component Containing functional
information for microarray data interpretation
- Functional data
- Individual gene analysis
- Pathway-based analysis
- Gene Ontology based analysis
- Linking expression data to the traditional
toxicological data
Microarray DB
LIB
10TOOL Component- Containing functionality for
microarray data analysis
- Analysis tools
- Four normalization methods
- Mean/median scaling for affy data
- LOWESS for 2-color array
- Gene selection method
- T-test, permutation t-test,
- Filtering using fold changes, intensity, flag inf
- Volcano plot, p-value plot
- Data exploring (e.g., HCA, PCA)
- Many visualization tools (e.g., flexible scatter
plot, Bar chart viewer,
TOOL
Microarray DB
LIB
11Supporting Eight Platforms
- Affy, Agilent, ABI, Combimatrix, Eppendorf, GE
Healthcare, Illumina and customized arrays - Affy data
- Probe data (.cel file)
- Probe-set data
Individual hyb import
Batch import
12TOOL
Microarray DB
LIB
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14Data Interpretation- GO-based analysis using
GOFFA
- GOFFA Gene Ontology For Functional Analysis
- It is developed based on Gene Ontology (GO)
database - Important for grouping the genes into functional
classes - GO Three ontologies
- Molecular function activities performed by
individual gene products at the molecular level,
such as catalytic activity, transporter activity,
binding - Biological process broad biological goals
accomplished by ordered assemblies of molecular
functions, such as cell growth, signal
transduction, metabolism - Cellular component the place in the cell where a
gene product is found, such as nucleus, ribosome,
proteasome
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16Data Interpretation
- Pathway-based tools
- Ingenuity Pathways Analysis
- KEGG
- PathArt
GOFFA Gene Ontology-based tool
Gene Annotation
17Ingenuity Pathways Analysis (IPA)
Ingenuity Pathways Analysis
Conduct statistical analysis
Interrogate genes or proteins on omics scale
Elucidate functional pathways
Understand markers of efficacy and safety
- KEGG and PathArt provide canonical pathways
- IPA provides both canonical and de-novo pathways
18Review Tool for Pharmacogenomics Data Submission
ArrayTrack
Receive the data support future regulatory policy
Verify the biological interpretation
Analyze the data
Microarray DB
Lib
Tool
Data repository
Analysis
Interpretation
ArrayTrack Components
19Future Direction - Toxicoinformatics Integrated
System (TIS)
GeneTools
Microarray DB
ProteinLib
PathwayLib
GeneLib
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21ArrayTrack Summary
- An integrated solution for microarray data
management, analysis and interpretation - Review tool for FDA pharmacogenomics data
submission - Training course is provided to the FDA reviewers
every two months - At present, 40 reviewers has been trained
- Freely available to public (http//edkb.fda.gov/we
bstart/arraytrack) - Users at big Pharma, academic and government
institutions U.S., Europe Asia
22ArrayTrack Tutorial
Topics Contents
1. (Basic) Comparing two groups (e.g., treated vs control groups) Statistical methods (t-test, permutation t-test, ANOVA) for group comparison. Differentially Expressed Genes (DEGs) identification Biological interpretation (individual gene analysis) using LIB Pathway analysis (KEGG, PathArt, IPA, MetaCore, Key Molnet) Gene Ontology analysis using GOFFA
2. Comparing multiple groups (e.g., multiple doses, time points)
3 VennDiagram Determine the common genes/pathways/functions shared by two or three gene lists (extended to cross-experiment and platform comparison and systems biology) Apply VennDiagram to the external files
4 Data exploring tools Principal Component Analysis (PCA) Hierarchical Cluster Analysis (HCA) Apply HCA and PCA to the external files Extensive features in HCA
23Topics Contents
5 Assessing gene expression profiles using BarChart Access BarChart from the TOOL box Access BarChart from the t-test result table Access BarChart from ChipLib and other Libs How to use BarChart for cross-experiment comparison Assign group by color
6 GeneList An important concept in ArrayTrack Create a gene list through data filtering and statistical analysis Import/export a gene list Conduct normalization filtered by a gene lists Conduct statistical analysis (t-test/ANOVA, PCA, HCA and others) based on a gene list Export a dataset by specifying the gene list (extended for cross-platform and cross-experiment comparison)
7 Normalization methods For Affymetrix platform MAS5, RMA, DChip, Plier, Plier16 For other platforms 7 methods (e.g., LOWESS)
8 How to create your own workspace Copy/Paste/duplicate an experiment
249 Import/Export Manual import and batch import Options of data exporting Export a selected dataset with specifying a sub list of gene Export multiple experiments and/or platforms using selected geneID types (e.g., RefSeq)
10 Other useful functions Correlation matrix IDConverter converting one gene ID to another (e.g., from AffyID to AgilentID or GeneBank, or LocusLinkID or vice verse) ScatterPlot pair-wise plot JoinTable Combine two tables SplitTable If a table contains multiple hybridization data in column with genes in row, the function split the table into individual tables with single hybridization data. GetUniqueID If a table contains duplicated IDs, the function pick out the unique IDs
11 Basic scripting for querying (raw and normalized) data and table Query data from the database tree (How to use ) e.g., EST, EST, ESTEST Query data in tables e.g., contain, like () and inlist